BadVLA: Towards Backdoor Attacks on Vision-Language-Action Models via Objective-Decoupled Optimization (2505.16640v1)

Abstract: Vision-Language-Action (VLA) models have advanced robotic control by enabling end-to-end decision-making directly from multimodal inputs. However, their tightly coupled architectures expose novel security vulnerabilities. Unlike traditional adversarial perturbations, backdoor attacks represent a stealthier, persistent, and practically significant threat-particularly under the emerging Training-as-a-Service paradigm-but remain largely unexplored in the context of VLA models. To address this gap, we propose BadVLA, a backdoor attack method based on Objective-Decoupled Optimization, which for the first time exposes the backdoor vulnerabilities of VLA models. Specifically, it consists of a two-stage process: (1) explicit feature-space separation to isolate trigger representations from benign inputs, and (2) conditional control deviations that activate only in the presence of the trigger, while preserving clean-task performance. Empirical results on multiple VLA benchmarks demonstrate that BadVLA consistently achieves near-100% attack success rates with minimal impact on clean task accuracy. Further analyses confirm its robustness against common input perturbations, task transfers, and model fine-tuning, underscoring critical security vulnerabilities in current VLA deployments. Our work offers the first systematic investigation of backdoor vulnerabilities in VLA models, highlighting an urgent need for secure and trustworthy embodied model design practices. We have released the project page at https://badvla-project.github.io/.

Overview of BackVLA: Backdoor Attacks on Vision-Language-Action Models

The paper "BadVLA: Towards Backdoor Attacks on Vision-Language-Action Models via Objective-Decoupled Optimization" presents an in-depth investigation into the vulnerabilities of Vision-Language-Action (VLA) models, which are pivotal in robotic control systems. VLA models integrate vision, language, and action modalities for end-to-end robotic policy learning. While these models offer impressive performance enhancements by eliminating the need for modular perception or planning, they also expose new security challenges.

Backdoor Vulnerabilities in VLA Models

The authors of the paper identify a critical threat emerging from the tightly coupled architectures of VLA models, particularly under the Training-as-a-Service (TaaS) paradigm. Unlike adversarial perturbations, backdoor attacks can be stealthier and more persistent, posing a significant threat when models are trained by external entities. The paper introduces BadVLA, a novel backdoor attack method employing Objective-Decoupled Optimization to expose these vulnerabilities. The method involves a two-stage optimization process aimed at isolating trigger representations in the feature space while ensuring that clean-task performance remains unaffected.

Methodology

BadVLA's attack strategy is innovative in its approach to disjunction between task and trigger objectives. The initial phase involves explicit feature-space separation facilitated by reference-aligned optimization, where trigger activations lead to significant divergence from clean input representations. The second phase focuses on preserving clean-task performance, drawing on insights from the decoupled optimization strategy's ability to maintain model stealth while introducing latent attack pathways.

Empirical Results

Empirical analysis across various VLA benchmarks demonstrates that BadVLA consistently achieves near-perfect attack success rates while minimally impacting clean-task accuracy. Notably, the paper reports an astonishingly high attack success rate of near-100% across tested benchmarks, underscoring serious security concerns in the current deployment of VLA models. The research further highlights the robustness of BadVLA against common defenses like input perturbations and task transfers, suggesting that current security measures may be inadequate for VLA-specific threats.

Implications and Future Directions

The findings have implications both for the practical deployment of VLA models and theoretical developments in AI security. Practically, the paper signals an urgent need for revising embodied model design practices to incorporate security considerations, such as defenses specifically tailored to mitigate backdoor vulnerabilities. Theoretically, it opens avenues for ongoing research into multimodal model security, advocating for robust training processes that can neutralize such entrenched threats. Future work may involve developing detection systems to systematically identify backdoor patterns or exploring architectural modifications to enhance model security.

Through its systematic exploration of these vulnerabilities, the paper establishes a foundational understanding of backdoor dynamics in multimodal systems, serving as a caution to developers and researchers in the robotic control domain. It challenges the status quo of VLA deployment, advocating for a shift towards secure, trustworthy model design and deployment practices.